Visualizing raw trial looking time
d_no_target <- d %>%
filter(trial_stimulus_type != "target")Looking time distribution
RAW
data_with_demog %>%
ggplot(aes(x = trial_looking_time)) +
geom_histogram(bins = 90) +
xlim(0, 9000)## Warning: Removed 72 rows containing non-finite values (stat_bin).## Warning: Removed 2 rows containing missing values (geom_bar).
data_with_demog %>%
ggplot(aes(x = trial_looking_time)) +
geom_histogram(bins = 90) +
xlim(0, 9000) +
facet_wrap(~subject)## Warning: Removed 72 rows containing non-finite values (stat_bin).## Warning: Removed 134 rows containing missing values (geom_bar).
AFTER EXCLUSION
d %>%
ggplot(aes(x = trial_looking_time)) +
geom_histogram(bins = 90) +
xlim(0, 6000)## Warning: Removed 2 rows containing missing values (geom_bar).
d %>%
ggplot(aes(x = trial_looking_time)) +
geom_histogram(bins = 90) +
xlim(0, 6000) +
facet_wrap(~subject)## Warning: Removed 84 rows containing missing values (geom_bar).
Exclude the target trial
d_no_target %>%
ggplot(aes(x = trial_looking_time)) +
geom_histogram(bins = 90) +
xlim(0, 6000)## Warning: Removed 2 rows containing missing values (geom_bar).
d_no_target %>%
ggplot(aes(x = trial_looking_time)) +
geom_histogram(bins = 90) +
xlim(0, 6000) +
facet_wrap(~subject)## Warning: Removed 84 rows containing missing values (geom_bar).
complexity difference
d %>%
ggplot(aes(x = trial_looking_time,
fill = trial_stimulus_complexity),
) +
geom_density(alpha = 0.5)+
xlim(0, 6000)
d %>%
ggplot(aes(x = trial_looking_time,
fill = trial_stimulus_complexity),
) +
geom_density(alpha = 0.5)+
xlim(0, 6000) +
facet_wrap(~subject)
block difference
d %>%
ggplot(aes(x = trial_looking_time,
fill = block),
) +
geom_density(alpha = 0.5)+
xlim(0, 6000)
d %>%
ggplot(aes(x = trial_looking_time,
fill = block),
) +
geom_density(alpha = 0.5)+
xlim(0, 6000) +
facet_wrap(~subject)
Complexity differences no target
d_no_target %>%
ggplot(aes(x = trial_looking_time,
fill = trial_stimulus_complexity),
) +
geom_density(alpha = 0.5)+
xlim(0, 6000)
d_no_target %>%
ggplot(aes(x = trial_looking_time,
fill = trial_stimulus_complexity),
) +
geom_density(alpha = 0.5)+
xlim(0, 6000) +
facet_wrap(~subject)
Block differences no target
d_no_target %>%
ggplot(aes(x = trial_looking_time,
fill = block),
) +
geom_density(alpha = 0.5)+
xlim(0, 6000)
d_no_target %>%
ggplot(aes(x = trial_looking_time,
fill = block),
) +
geom_density(alpha = 0.5)+
xlim(0, 6000) +
facet_wrap(~subject)## Warning: Groups with fewer than two data points have been dropped.## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning -
## Inf
Visualizing aggregarded looking time
d_sum_individual <- d %>%
group_by(subject, block) %>%
summarise(
mean_lt = mean(trial_looking_time, na.rm = TRUE),
sd = sd(trial_looking_time, na.rm = TRUE),
n = n(),
ci_range_95 = qt(1 - (0.05 / 2), n - 1) * (sd/sqrt(n)),
ci_ub = mean_lt + ci_range_95,
ci_lb = mean_lt - ci_range_95
)## `summarise()` regrouping output by 'subject' (override with `.groups` argument)d_sum <- d %>%
group_by(block) %>%
summarise(
mean_lt = mean(trial_looking_time, na.rm = TRUE),
sd = sd(trial_looking_time, na.rm = TRUE),
n = n(),
ci_range_95 = qt(1 - (0.05 / 2), n - 1) * (sd/sqrt(n)),
ci_ub = mean_lt + ci_range_95,
ci_lb = mean_lt - ci_range_95
)## `summarise()` ungrouping output (override with `.groups` argument)aggregated
d_sum %>% ggplot(aes(x = block, y = mean_lt)) +
geom_pointrange(aes(ymin = ci_lb, ymax = ci_ub)) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
individual
something weird happened
d_sum_individual %>%
ggplot(aes(x = block, y = mean_lt)) +
geom_pointrange(aes(ymin = ci_lb, ymax = ci_ub)) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
d_sum_individual %>%
ggplot(aes(x = block, y = mean_lt)) +
geom_pointrange(aes(ymin = ci_lb, ymax = ci_ub)) +
facet_wrap(~subject) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
weird ones
We can see three very weird ones: SS1604513317537 SS1604515995769 SS1604516882157
weird <- c("SS1604513317537",
"SS1604515995769",
"SS1604516660396")
d_sum_individual %>%
filter(subject %in% weird) %>%
ggplot(aes(x = block, y = mean_lt)) +
geom_pointrange(aes(ymin = ci_lb, ymax = ci_ub)) +
facet_wrap(~subject) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
d %>%
filter(subject %in% weird) %>%
ggplot(aes(x = trial_looking_time)) +
geom_histogram(bins = 90) +
facet_wrap(~subject) +
xlim(0, 6000)+
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))## Warning: Removed 6 rows containing missing values (geom_bar).
Excluding weird ones
d_no_weird <- d %>%
filter(!(subject %in% weird))
d_no_weird_sum <- d_no_weird %>%
group_by(block) %>%
summarise(
mean_lt = mean(trial_looking_time, na.rm = TRUE),
sd = sd(trial_looking_time, na.rm = TRUE),
n = n(),
ci_range_95 = qt(1 - (0.05 / 2), n - 1) * (sd/sqrt(n)),
ci_ub = mean_lt + ci_range_95,
ci_lb = mean_lt - ci_range_95
)## `summarise()` ungrouping output (override with `.groups` argument)d_no_weird_sum %>% ggplot(aes(x = block, y = mean_lt)) +
geom_pointrange(aes(ymin = ci_lb, ymax = ci_ub)) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
Demographic
SES
education == “Some high school” ~ 1, education == “High school diploma” ~ 2, education == “Associate Degree/Technical certification” ~ 3, education == “Bachelor’s Degree” ~ 4, education == “Master’s Degree” ~ 5, education == “Doctorate/Professional degree” ~ 6, education == “Other” ~ NA_real_
d %>%
distinct(subject, .keep_all = TRUE) %>%
ggplot(aes(x = demog_education)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 4 rows containing non-finite values (stat_bin).
null_m <- lmer(trial_looking_time ~ 1 + (1|subject),
data = filter(d, !is.na(demog_education)))
edu_m <- lmer(trial_looking_time ~ demog_education + (1|subject),
data = filter(d, !is.na(demog_education)))
summary(edu_m)## Linear mixed model fit by REML ['lmerMod']
## Formula: trial_looking_time ~ demog_education + (1 | subject)
## Data: filter(d, !is.na(demog_education))
##
## REML criterion at convergence: 114420.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8912 -0.4691 -0.2950 0.0165 7.5395
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 58642 242.2
## Residual 331678 575.9
## Number of obs: 7351, groups: subject, 38
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 450.69 214.44 2.102
## demog_education 71.82 54.47 1.319
##
## Correlation of Fixed Effects:
## (Intr)
## demog_edctn -0.983anova(null_m, edu_m)## refitting model(s) with ML (instead of REML)## Data: filter(d, !is.na(demog_education))
## Models:
## null_m: trial_looking_time ~ 1 + (1 | subject)
## edu_m: trial_looking_time ~ demog_education + (1 | subject)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## null_m 3 114448 114468 -57221 114442
## edu_m 4 114448 114475 -57220 114440 1.7923 1 0.1807Age
d %>%
distinct(subject, .keep_all = TRUE) %>%
ggplot(aes(x = demog_age)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Warning: Removed 4 rows containing non-finite values (stat_bin).
null_m <- lmer(trial_looking_time ~ 1 + (1|subject),
data = filter(d, !is.na(demog_age)))
age_m <- lmer(trial_looking_time ~ demog_age + (1|subject),
data = filter(d, !is.na(demog_age)))## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00430357 (tol = 0.002, component 1)summary(age_m)## Linear mixed model fit by REML ['lmerMod']
## Formula: trial_looking_time ~ demog_age + (1 | subject)
## Data: filter(d, !is.na(demog_age))
##
## REML criterion at convergence: 114427.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8892 -0.4699 -0.2950 0.0175 7.5389
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 60451 245.9
## Residual 331679 575.9
## Number of obs: 7351, groups: subject, 38
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 637.516 123.180 5.175
## demog_age 2.458 3.142 0.782
##
## Correlation of Fixed Effects:
## (Intr)
## demog_age -0.945
## convergence code: 0
## Model failed to converge with max|grad| = 0.00430357 (tol = 0.002, component 1)anova(null_m, age_m)## refitting model(s) with ML (instead of REML)## Data: filter(d, !is.na(demog_age))
## Models:
## null_m: trial_looking_time ~ 1 + (1 | subject)
## age_m: trial_looking_time ~ demog_age + (1 | subject)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## null_m 3 114448 114468 -57221 114442
## age_m 4 114449 114477 -57220 114441 0.6398 1 0.4238number of background repetition on looking time
full, aggregated, by complexity
full_aggregated <- d %>%
mutate(
number = 1) %>%
group_by(
subject, block, trial_stimulus_type
) %>%
mutate(num_times_stimulus_seen = cumsum(number))
full_aggregated %>%
filter(trial_stimulus_type == "background") %>%
ggplot(
aes(y = log(trial_looking_time),
x = num_times_stimulus_seen,
color = trial_stimulus_complexity)
) +
geom_point(aes(alpha = 0.2), size = 3, shape = ".") +
guides(alpha = FALSE) +
labs(color = "Stimulus Complexity") +
ylab("Mean Looking Time (log)") +
xlab("Number of Stimulus Reptitions") +
geom_smooth(method = "lm") +
theme(axis.text = element_text(size = 10))## `geom_smooth()` using formula 'y ~ x'
full, aggregated, by blocks
full_aggregated %>%
filter(trial_stimulus_type == "background") %>%
ggplot(
aes(y = log(trial_looking_time),
x = num_times_stimulus_seen,
color = block)
) +
geom_point(aes(alpha = 0.2), size = 3, shape = ".") +
guides(alpha = FALSE) +
labs(color = "Stimulus Complexity") +
ylab("Mean Looking Time (log)") +
xlab("Number of Stimulus Reptitions") +
geom_smooth(method = "lm") +
theme(axis.text = element_text(size = 10))## `geom_smooth()` using formula 'y ~ x'
no weird, aggregated, by complexity
excluded_sum <- full_aggregated %>%
filter(trial_stimulus_type == "background") %>%
filter(!(subject %in% weird))
excluded_sum %>%
ggplot(
aes(y = log(trial_looking_time),
x = num_times_stimulus_seen,
color = trial_stimulus_complexity)
) +
geom_point(aes(alpha = 0.2), size = 3, shape = ".") +
guides(alpha = FALSE) +
labs(color = "Stimulus Complexity") +
ylab("Mean Looking Time (log)") +
xlab("Number of Stimulus Reptitions") +
geom_smooth(method = "lm") +
theme(axis.text = element_text(size = 10))## `geom_smooth()` using formula 'y ~ x'
no weird, aggregated, by blocks
excluded_sum %>%
ggplot(
aes(y = log(trial_looking_time),
x = num_times_stimulus_seen,
color = block)
) +
geom_point(aes(alpha = 0.2), size = 3, shape = ".") +
guides(alpha = FALSE) +
labs(color = "Stimulus Complexity") +
ylab("Mean Looking Time (log)") +
xlab("Number of Stimulus Reptitions") +
geom_smooth(method = "lm") +
theme(axis.text = element_text(size = 10))## `geom_smooth()` using formula 'y ~ x'
individual, by complexity
full_aggregated %>%
filter(trial_stimulus_type == "background") %>%
ggplot(
aes(y = log(trial_looking_time),
x = num_times_stimulus_seen,
color = trial_stimulus_complexity)
) +
geom_point(aes(alpha = 0.2), size = 3, shape = ".") +
guides(alpha = FALSE) +
labs(color = "Stimulus Complexity") +
ylab("Mean Looking Time (log)") +
xlab("Number of Stimulus Reptitions") +
geom_smooth(method = "lm") +
theme(axis.text = element_text(size = 10)) +
facet_wrap(~subject)## `geom_smooth()` using formula 'y ~ x'
individual, by block
full_aggregated %>%
filter(trial_stimulus_type == "background") %>%
ggplot(
aes(y = log(trial_looking_time),
x = num_times_stimulus_seen,
color = block)
) +
geom_point(aes(alpha = 0.2), size = 3, shape = ".") +
guides(alpha = FALSE) +
labs(color = "Stimulus Complexity") +
ylab("Mean Looking Time (log)") +
xlab("Number of Stimulus Reptitions") +
geom_smooth(method = "lm") +
theme(axis.text = element_text(size = 10)) +
facet_wrap(~subject)## `geom_smooth()` using formula 'y ~ x'
Model
linear
block
null_m <- lmer(trial_looking_time ~ 1 + (1|subject),
data = d)
basic_m <- lmer(trial_looking_time ~ block + (1|subject),
data = d)
anova(null_m, basic_m)## refitting model(s) with ML (instead of REML)## Data: d
## Models:
## null_m: trial_looking_time ~ 1 + (1 | subject)
## basic_m: trial_looking_time ~ block + (1 | subject)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## null_m 3 121133 121154 -60563 121127
## basic_m 6 121126 121167 -60557 121114 13.076 3 0.004474 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1stimuli repetition
null_m <- lmer(log(trial_looking_time) ~ 1 + (1|subject),
data = excluded_sum)
rep_m <- lmer(log(trial_looking_time) ~ num_times_stimulus_seen + (1|subject),
data = excluded_sum)stimuli repetition and block interaction
interaction_m <- lmer(log(trial_looking_time) ~ num_times_stimulus_seen * block + (1|subject),
data = excluded_sum)
anova(rep_m, null_m)## refitting model(s) with ML (instead of REML)## Data: excluded_sum
## Models:
## null_m: log(trial_looking_time) ~ 1 + (1 | subject)
## rep_m: log(trial_looking_time) ~ num_times_stimulus_seen + (1 | subject)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## null_m 3 6049.9 6069.7 -3022.0 6043.9
## rep_m 4 5984.9 6011.3 -2988.5 5976.9 67.016 1 2.693e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(rep_m, interaction_m)## refitting model(s) with ML (instead of REML)## Data: excluded_sum
## Models:
## rep_m: log(trial_looking_time) ~ num_times_stimulus_seen + (1 | subject)
## interaction_m: log(trial_looking_time) ~ num_times_stimulus_seen * block + (1 |
## interaction_m: subject)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## rep_m 4 5984.9 6011.3 -2988.5 5976.9
## interaction_m 10 5980.6 6046.5 -2980.3 5960.6 16.322 6 0.01213 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(rep_m)## Linear mixed model fit by REML ['lmerMod']
## Formula: log(trial_looking_time) ~ num_times_stimulus_seen + (1 | subject)
## Data: excluded_sum
##
## REML criterion at convergence: 5994.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7972 -0.5324 -0.1798 0.2486 6.3457
##
## Random effects:
## Groups Name Variance Std.Dev.
## subject (Intercept) 0.1051 0.3241
## Residual 0.1726 0.4155
## Number of obs: 5370, groups: subject, 39
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 6.3118005 0.0531764 118.695
## num_times_stimulus_seen -0.0046543 0.0005668 -8.211
##
## Correlation of Fixed Effects:
## (Intr)
## nm_tms_stm_ -0.189GAM
only focusing on looking time at the background
library(mgcv)## Loading required package: nlme##
## Attaching package: 'nlme'## The following object is masked from 'package:lme4':
##
## lmList## The following object is masked from 'package:dplyr':
##
## collapse## This is mgcv 1.8-33. For overview type 'help("mgcv-package")'.gam_d <- full_aggregated %>%
filter(trial_stimulus_type == "background") %>%
mutate(trial_stimulus_complexity = as.factor(trial_stimulus_complexity),
block = as.factor(block))number of repetition
gam_m <- gam(trial_looking_time ~ s(num_times_stimulus_seen),
data = gam_d,
method = "REML")
summary(gam_m)##
## Family: gaussian
## Link function: identity
##
## Formula:
## trial_looking_time ~ s(num_times_stimulus_seen)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 604.987 6.635 91.19 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(num_times_stimulus_seen) 7.879 8.672 21.27 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0324 Deviance explained = 3.38%
## -REML = 42462 Scale est. = 2.4513e+05 n = 5569plot(gam_m,
pages = 1,
se = TRUE,
shade = TRUE)
gam.check(gam_m)
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.0001646932,2.934368e-05]
## (score 42462.4 & scale 245132.1).
## Hessian positive definite, eigenvalue range [2.4001,2783.504].
## Model rank = 10 / 10
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(num_times_stimulus_seen) 9.00 7.88 0.99 0.15concurvity(gam_m, full = TRUE)## para s(num_times_stimulus_seen)
## worst 5.278689e-27 5.196644e-27
## observed 5.278689e-27 7.734321e-28
## estimate 5.278689e-27 2.303504e-29number of repetition (with log?)
gam_m <- gam(log(trial_looking_time) ~ s(num_times_stimulus_seen),
data = gam_d,
method = "REML")
summary(gam_m)##
## Family: gaussian
## Link function: identity
##
## Formula:
## log(trial_looking_time) ~ s(num_times_stimulus_seen)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.228538 0.006969 893.8 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(num_times_stimulus_seen) 7.703 8.572 20.92 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0315 Deviance explained = 3.29%
## -REML = 4279.6 Scale est. = 0.27043 n = 5569plot(gam_m,
pages = 1,
se = TRUE,
shade = TRUE)
gam.check(gam_m)
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.0003978788,-7.401346e-08]
## (score 4279.63 & scale 0.270434).
## Hessian positive definite, eigenvalue range [2.26755,2783.504].
## Model rank = 10 / 10
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(num_times_stimulus_seen) 9.0 7.7 0.98 0.045 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1concurvity(gam_m, full = TRUE)## para s(num_times_stimulus_seen)
## worst 5.278689e-27 5.196644e-27
## observed 5.278689e-27 5.868023e-28
## estimate 5.278689e-27 2.303504e-29number of repetition by stimulus complexity
gam_m <- gam(trial_looking_time ~ s(num_times_stimulus_seen,
by = trial_stimulus_complexity),
data = gam_d,
method = "REML")
summary(gam_m)##
## Family: gaussian
## Link function: identity
##
## Formula:
## trial_looking_time ~ s(num_times_stimulus_seen, by = trial_stimulus_complexity)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 604.963 6.635 91.18 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F
## s(num_times_stimulus_seen):trial_stimulus_complexitycomplex 6.950 8.031 9.027
## s(num_times_stimulus_seen):trial_stimulus_complexitysimple 7.233 8.253 13.584
## p-value
## s(num_times_stimulus_seen):trial_stimulus_complexitycomplex <2e-16 ***
## s(num_times_stimulus_seen):trial_stimulus_complexitysimple <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0325 Deviance explained = 3.49%
## -REML = 42465 Scale est. = 2.4512e+05 n = 5569plot(gam_m,
pages = 1,
se = TRUE,
shade = TRUE)
gam.check(gam_m)
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.0001139121,4.48257e-06]
## (score 42465.35 & scale 245121.1).
## Hessian positive definite, eigenvalue range [1.675453,2783.007].
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index
## s(num_times_stimulus_seen):trial_stimulus_complexitycomplex 9.00 6.95 1.02
## s(num_times_stimulus_seen):trial_stimulus_complexitysimple 9.00 7.23 1.02
## p-value
## s(num_times_stimulus_seen):trial_stimulus_complexitycomplex 0.86
## s(num_times_stimulus_seen):trial_stimulus_complexitysimple 0.86concurvity(gam_m, full = TRUE)## para
## worst 7.391881e-05
## observed 7.391881e-05
## estimate 7.391881e-05
## s(num_times_stimulus_seen):trial_stimulus_complexitycomplex
## worst 3.694972e-05
## observed 1.413731e-07
## estimate 1.013735e-05
## s(num_times_stimulus_seen):trial_stimulus_complexitysimple
## worst 3.697182e-05
## observed 2.975684e-09
## estimate 1.001321e-05number of repetition by stimulus complexity (log?)
gam_m <- gam(log(trial_looking_time) ~ s(num_times_stimulus_seen,
by = trial_stimulus_complexity),
data = gam_d,
method = "REML")
summary(gam_m)##
## Family: gaussian
## Link function: identity
##
## Formula:
## log(trial_looking_time) ~ s(num_times_stimulus_seen, by = trial_stimulus_complexity)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.228497 0.006967 894.1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F
## s(num_times_stimulus_seen):trial_stimulus_complexitycomplex 6.517 7.656 7.622
## s(num_times_stimulus_seen):trial_stimulus_complexitysimple 7.146 8.187 15.219
## p-value
## s(num_times_stimulus_seen):trial_stimulus_complexitycomplex <2e-16 ***
## s(num_times_stimulus_seen):trial_stimulus_complexitysimple <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0322 Deviance explained = 3.45%
## -REML = 4287.1 Scale est. = 0.27026 n = 5569plot(gam_m,
pages = 1,
se = TRUE,
shade = TRUE)
gam.check(gam_m)
##
## Method: REML Optimizer: outer newton
## full convergence after 6 iterations.
## Gradient range [-0.0004995101,2.60902e-05]
## (score 4287.147 & scale 0.2702625).
## Hessian positive definite, eigenvalue range [1.622075,2783.007].
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index
## s(num_times_stimulus_seen):trial_stimulus_complexitycomplex 9.00 6.52 1.02
## s(num_times_stimulus_seen):trial_stimulus_complexitysimple 9.00 7.15 1.02
## p-value
## s(num_times_stimulus_seen):trial_stimulus_complexitycomplex 0.90
## s(num_times_stimulus_seen):trial_stimulus_complexitysimple 0.88concurvity(gam_m, full = TRUE)## para
## worst 7.391881e-05
## observed 7.391881e-05
## estimate 7.391881e-05
## s(num_times_stimulus_seen):trial_stimulus_complexitycomplex
## worst 3.694972e-05
## observed 1.021689e-07
## estimate 1.013735e-05
## s(num_times_stimulus_seen):trial_stimulus_complexitysimple
## worst 3.697182e-05
## observed 1.087388e-07
## estimate 1.001321e-05number of repetition by block
gam_m <- gam(trial_looking_time ~ s(num_times_stimulus_seen, by = block),
data = gam_d,
method = "REML")
plot(gam_m, all.terms = TRUE, pages = 1,
shade = TRUE,
rug = TRUE,
se = TRUE)
summary(gam_m)##
## Family: gaussian
## Link function: identity
##
## Formula:
## trial_looking_time ~ s(num_times_stimulus_seen, by = block)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 604.966 6.632 91.22 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F
## s(num_times_stimulus_seen):blockall_complex 5.876 7.035 4.642
## s(num_times_stimulus_seen):blockall_simple 7.234 8.255 10.986
## s(num_times_stimulus_seen):blockmixed_complex_deviant 4.119 5.080 6.754
## s(num_times_stimulus_seen):blockmixed_simple_deviant 5.588 6.735 4.952
## p-value
## s(num_times_stimulus_seen):blockall_complex 3.15e-05 ***
## s(num_times_stimulus_seen):blockall_simple < 2e-16 ***
## s(num_times_stimulus_seen):blockmixed_complex_deviant 2.17e-06 ***
## s(num_times_stimulus_seen):blockmixed_simple_deviant 2.04e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0333 Deviance explained = 3.72%
## -REML = 42462 Scale est. = 2.4492e+05 n = 5569gam.check(gam_m)
##
## Method: REML Optimizer: outer newton
## full convergence after 8 iterations.
## Gradient range [-0.0004214249,4.232332e-05]
## (score 42461.66 & scale 244917.5).
## Hessian positive definite, eigenvalue range [0.9508617,2782.009].
## Model rank = 37 / 37
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(num_times_stimulus_seen):blockall_complex 9.00 5.88 1 0.43
## s(num_times_stimulus_seen):blockall_simple 9.00 7.23 1 0.44
## s(num_times_stimulus_seen):blockmixed_complex_deviant 9.00 4.12 1 0.36
## s(num_times_stimulus_seen):blockmixed_simple_deviant 9.00 5.59 1 0.33concurvity(gam_m, full = TRUE)## para s(num_times_stimulus_seen):blockall_complex
## worst 0.0001035554 2.539898e-05
## observed 0.0001035554 3.225119e-06
## estimate 0.0001035554 1.611805e-05
## s(num_times_stimulus_seen):blockall_simple
## worst 2.329026e-05
## observed 5.265772e-09
## estimate 4.459062e-06
## s(num_times_stimulus_seen):blockmixed_complex_deviant
## worst 1.697086e-05
## observed 1.606767e-07
## estimate 5.689955e-06
## s(num_times_stimulus_seen):blockmixed_simple_deviant
## worst 3.790317e-05
## observed 6.899570e-06
## estimate 2.979582e-06number of repetition by block (log?)
gam_m <- gam(log(trial_looking_time) ~ s(num_times_stimulus_seen, by = block),
data = gam_d,
method = "REML")
plot(gam_m, all.terms = TRUE, pages = 1,
shade = TRUE,
rug = TRUE,
se = TRUE)
summary(gam_m)##
## Family: gaussian
## Link function: identity
##
## Formula:
## log(trial_looking_time) ~ s(num_times_stimulus_seen, by = block)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.22852 0.00697 893.6 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F
## s(num_times_stimulus_seen):blockall_complex 4.655 5.705 4.434
## s(num_times_stimulus_seen):blockall_simple 6.808 7.913 11.433
## s(num_times_stimulus_seen):blockmixed_complex_deviant 4.471 5.493 7.375
## s(num_times_stimulus_seen):blockmixed_simple_deviant 5.068 6.171 3.938
## p-value
## s(num_times_stimulus_seen):blockall_complex 0.000393 ***
## s(num_times_stimulus_seen):blockall_simple < 2e-16 ***
## s(num_times_stimulus_seen):blockmixed_complex_deviant 1.3e-06 ***
## s(num_times_stimulus_seen):blockmixed_simple_deviant 0.000487 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0312 Deviance explained = 3.49%
## -REML = 4299.7 Scale est. = 0.27053 n = 5569gam.check(gam_m)
##
## Method: REML Optimizer: outer newton
## full convergence after 7 iterations.
## Gradient range [-0.0005736542,3.267841e-05]
## (score 4299.665 & scale 0.2705294).
## Hessian positive definite, eigenvalue range [0.486995,2782.007].
## Model rank = 37 / 37
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(num_times_stimulus_seen):blockall_complex 9.00 4.66 0.99 0.34
## s(num_times_stimulus_seen):blockall_simple 9.00 6.81 0.99 0.27
## s(num_times_stimulus_seen):blockmixed_complex_deviant 9.00 4.47 0.99 0.26
## s(num_times_stimulus_seen):blockmixed_simple_deviant 9.00 5.07 0.99 0.31concurvity(gam_m, full = TRUE)## para s(num_times_stimulus_seen):blockall_complex
## worst 0.0001035554 2.539898e-05
## observed 0.0001035554 6.175376e-06
## estimate 0.0001035554 1.611805e-05
## s(num_times_stimulus_seen):blockall_simple
## worst 2.329026e-05
## observed 2.196112e-08
## estimate 4.459062e-06
## s(num_times_stimulus_seen):blockmixed_complex_deviant
## worst 1.697086e-05
## observed 4.328620e-10
## estimate 5.689955e-06
## s(num_times_stimulus_seen):blockmixed_simple_deviant
## worst 3.790317e-05
## observed 7.738447e-06
## estimate 2.979582e-06